LM_diagnostics
==============

.. py:module:: LM_diagnostics


Attributes
----------

.. autoapisummary::

   LM_diagnostics.style_talk


Classes
-------

.. autoapisummary::

   LM_diagnostics.LinearRegDiagnostic


Module Contents
---------------

.. py:data:: style_talk
   :value: 'seaborn-talk'


.. py:class:: LinearRegDiagnostic(results: Type[statsmodels.regression.linear_model.RegressionResultsWrapper])

   Diagnostic plots to identify potential problems in a linear regression fit.
   Mainly,
       a. non-linearity of data
       b. Correlation of error terms
       c. non-constant variance
       d. outliers
       e. high-leverage points
       f. collinearity

   Authors:
       Prajwal Kafle (p33ajkafle@gmail.com, where 3 = r)
       Does not come with any sort of warranty.
       Please test the code one your end before using.

       Matt Spinelli (m3spinelli@gmail.com, where 3 = r)
       (1) Fixed incorrect annotation of the top most extreme residuals in
           the Residuals vs Fitted and, especially, the Normal Q-Q plots.
       (2) Changed Residuals vs Leverage plot to match closer the y-axis
           range shown in the equivalent plot in the R package ggfortify.
       (3) Added horizontal line at y=0 in Residuals vs Leverage plot to
           match the plots in R package ggfortify and base R.
       (4) Added option for placing a vertical guideline on the Residuals
           vs Leverage plot using the rule of thumb of h = 2p/n to denote
           high leverage (high_leverage_threshold=True).
       (5) Added two more ways to compute the Cook's Distance (D) threshold:
           * 'baseR': D > 1 and D > 0.5 (default)
           * 'convention': D > 4/n
           * 'dof': D > 4 / (n - k - 1)
       (6) Fixed class name to conform to Pascal casing convention
       (7) Fixed Residuals vs Leverage legend to work with loc='best'


   .. py:attribute:: results


   .. py:attribute:: y_true


   .. py:attribute:: y_predict


   .. py:attribute:: xvar


   .. py:attribute:: xvar_names


   .. py:attribute:: residual


   .. py:attribute:: residual_norm


   .. py:attribute:: leverage


   .. py:attribute:: cooks_distance


   .. py:attribute:: nparams


   .. py:attribute:: nresids


   .. py:method:: __call__(plot_context='seaborn-v0_8-paper', **kwargs)


   .. py:method:: residual_plot(ax=None)

      Residual vs Fitted Plot

      Graphical tool to identify non-linearity.
      (Roughly) Horizontal red line is an indicator that the residual has a linear pattern



   .. py:method:: qq_plot(ax=None)

      Standarized Residual vs Theoretical Quantile plot

      Used to visually check if residuals are normally distributed.
      Points spread along the diagonal line will suggest so.



   .. py:method:: scale_location_plot(ax=None)

      Sqrt(Standarized Residual) vs Fitted values plot

      Used to check homoscedasticity of the residuals.
      Horizontal line will suggest so.



   .. py:method:: leverage_plot(ax=None, high_leverage_threshold=False, cooks_threshold='baseR')

      Residual vs Leverage plot

      Points falling outside Cook's distance curves are considered observation that can sway the fit
      aka are influential.
      Good to have none outside the curves.



   .. py:method:: vif_table()

      VIF table

      VIF, the variance inflation factor, is a measure of multicollinearity.
      VIF > 5 for a variable indicates that it is highly collinear with the
      other input variables.



   .. py:method:: get_influence_ids(n_i=10)

      Get top 10 influential residuals



   .. py:method:: __cooks_dist_line(factor)

      Helper function for plotting Cook's distance curves



   .. py:method:: __qq_top_resid(quantiles, top_residual_indices)

      Helper generator function yielding the index and coordinates